# training with captions # Swap blocks between CPU and GPU: # This implementation is inspired by and based on the work of 2kpr. # Many thanks to 2kpr for the original concept and implementation of memory-efficient offloading. # The original idea has been adapted and extended to fit the current project's needs. # Key features: # - CPU offloading during forward and backward passes # - Use of fused optimizer and grad_hook for efficient gradient processing # - Per-block fused optimizer instances import argparse import copy import math import os from multiprocessing import Value import toml from tqdm import tqdm import torch from .library.device_utils import init_ipex, clean_memory_on_device init_ipex() from accelerate.utils import set_seed from .library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux from .library.sd3_train_utils import FlowMatchEulerDiscreteScheduler from .library import train_util as train_util from .library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) from .library import config_util as config_util from .library.config_util import ( ConfigSanitizer, BlueprintGenerator, ) from .library.custom_train_functions import apply_masked_loss, add_custom_train_arguments class FluxTrainer: def __init__(self): self.sample_prompts_te_outputs = None def sample_images(self, epoch, global_step, validation_settings): image_tensors = flux_train_utils.sample_images( self.accelerator, self.args, epoch, global_step, self.unet, self.vae, self.text_encoder, self.sample_prompts_te_outputs, validation_settings) return image_tensors def init_train(self, args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) # sdxl_train_util.verify_sdxl_training_args(args) deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) # temporary: backward compatibility for deprecated options. remove in the future if not args.skip_cache_check: args.skip_cache_check = args.skip_latents_validity_check if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: logger.warning( "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります" ) args.cache_text_encoder_outputs = True if args.cpu_offload_checkpointing and not args.gradient_checkpointing: logger.warning( "cpu_offload_checkpointing is enabled, so gradient_checkpointing is also enabled / cpu_offload_checkpointingが有効になっているため、gradient_checkpointingも有効になります" ) args.gradient_checkpointing = True assert ( args.blocks_to_swap is None or args.blocks_to_swap == 0 ) or not args.cpu_offload_checkpointing, ( "blocks_to_swap is not supported with cpu_offload_checkpointing / blocks_to_swapはcpu_offload_checkpointingと併用できません" ) cache_latents = args.cache_latents use_dreambooth_method = args.in_json is None if args.seed is not None: set_seed(args.seed) # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization. if args.cache_latents: latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy( args.cache_latents_to_disk, args.vae_batch_size, args.skip_latents_validity_check ) strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) # Prepare the dataset if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) if args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) ignored = ["train_data_dir", "in_json"] if any(getattr(args, attr) is not None for attr in ignored): logger.warning( "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( ", ".join(ignored) ) ) else: if use_dreambooth_method: logger.info("Using DreamBooth method.") user_config = { "datasets": [ { "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( args.train_data_dir, args.reg_data_dir ) } ] } else: logger.info("Training with captions.") user_config = { "datasets": [ { "subsets": [ { "image_dir": args.train_data_dir, "metadata_file": args.in_json, } ] } ] } blueprint = blueprint_generator.generate(user_config, args) train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: train_dataset_group = train_util.load_arbitrary_dataset(args) current_epoch = Value("i", 0) current_step = Value("i", 0) ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) train_dataset_group.verify_bucket_reso_steps(16) # TODO これでいいか確認 _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path) if args.debug_dataset: if args.cache_text_encoder_outputs: strategy_base.TextEncoderOutputsCachingStrategy.set_strategy( strategy_flux.FluxTextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, False ) ) t5xxl_max_token_length = ( args.t5xxl_max_token_length if args.t5xxl_max_token_length is not None else (256 if is_schnell else 512) ) strategy_base.TokenizeStrategy.set_strategy(strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)) train_dataset_group.set_current_strategies() train_util.debug_dataset(train_dataset_group, True) return if len(train_dataset_group) == 0: logger.error( "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。" ) return if cache_latents: assert ( train_dataset_group.is_latent_cacheable() ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" if args.cache_text_encoder_outputs: assert ( train_dataset_group.is_text_encoder_output_cacheable() ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" # acceleratorを準備する logger.info("prepare accelerator") accelerator = train_util.prepare_accelerator(args) # mixed precisionに対応した型を用意しておき適宜castする weight_dtype, save_dtype = train_util.prepare_dtype(args) # load VAE for caching latents ae = None if cache_latents: ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", args.disable_mmap_load_safetensors) ae.to(accelerator.device, dtype=weight_dtype) ae.requires_grad_(False) ae.eval() train_dataset_group.new_cache_latents(ae, accelerator) ae.to("cpu") # if no sampling, vae can be deleted clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() # prepare tokenize strategy if args.t5xxl_max_token_length is None: if is_schnell: t5xxl_max_token_length = 256 else: t5xxl_max_token_length = 512 else: t5xxl_max_token_length = args.t5xxl_max_token_length flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length) strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy) # load clip_l, t5xxl for caching text encoder outputs clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", args.disable_mmap_load_safetensors) t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu", args.disable_mmap_load_safetensors) clip_l.eval() t5xxl.eval() clip_l.requires_grad_(False) t5xxl.requires_grad_(False) text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask) strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy) # cache text encoder outputs sample_prompts_te_outputs = None if args.cache_text_encoder_outputs: # Text Encodes are eval and no grad here clip_l.to(accelerator.device) t5xxl.to(accelerator.device) text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask ) strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy) with accelerator.autocast(): train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator) # cache sample prompt's embeddings to free text encoder's memory if args.sample_prompts is not None: logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}") text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy() prompts = [] for line in args.sample_prompts: line = line.strip() if len(line) > 0 and line[0] != "#": prompts.append(line) # preprocess prompts for i in range(len(prompts)): prompt_dict = prompts[i] if isinstance(prompt_dict, str): from .library.train_util import line_to_prompt_dict prompt_dict = line_to_prompt_dict(prompt_dict) prompts[i] = prompt_dict assert isinstance(prompt_dict, dict) # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict. prompt_dict["enum"] = i prompt_dict.pop("subset", None) sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs with accelerator.autocast(), torch.no_grad(): for prompt_dict in prompts: for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: if p not in sample_prompts_te_outputs: logger.info(f"cache Text Encoder outputs for prompt: {p}") tokens_and_masks = flux_tokenize_strategy.tokenize(p) sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens( flux_tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask ) self.sample_prompts_te_outputs = sample_prompts_te_outputs accelerator.wait_for_everyone() # now we can delete Text Encoders to free memory clip_l = None t5xxl = None clean_memory_on_device(accelerator.device) # load FLUX _, flux = flux_utils.load_flow_model( args.pretrained_model_name_or_path, weight_dtype, "cpu", args.disable_mmap_load_safetensors ) if args.gradient_checkpointing: flux.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing) flux.requires_grad_(True) # block swap # backward compatibility if args.blocks_to_swap is None: blocks_to_swap = args.double_blocks_to_swap or 0 if args.single_blocks_to_swap is not None: blocks_to_swap += args.single_blocks_to_swap // 2 if blocks_to_swap > 0: logger.warning( "double_blocks_to_swap and single_blocks_to_swap are deprecated. Use blocks_to_swap instead." " / double_blocks_to_swapとsingle_blocks_to_swapは非推奨です。blocks_to_swapを使ってください。" ) logger.info( f"double_blocks_to_swap={args.double_blocks_to_swap} and single_blocks_to_swap={args.single_blocks_to_swap} are converted to blocks_to_swap={blocks_to_swap}." ) args.blocks_to_swap = blocks_to_swap del blocks_to_swap self.is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0 if self.is_swapping_blocks: # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes. # This idea is based on 2kpr's great work. Thank you! logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}") flux.enable_block_swap(args.blocks_to_swap, accelerator.device) if not cache_latents: # load VAE here if not cached ae = flux_utils.load_ae(args.ae, weight_dtype, "cpu") ae.requires_grad_(False) ae.eval() ae.to(accelerator.device, dtype=weight_dtype) training_models = [] params_to_optimize = [] training_models.append(flux) name_and_params = list(flux.named_parameters()) # single param group for now params_to_optimize.append({"params": [p for _, p in name_and_params], "lr": args.learning_rate}) param_names = [[n for n, _ in name_and_params]] # calculate number of trainable parameters n_params = 0 for group in params_to_optimize: for p in group["params"]: n_params += p.numel() accelerator.print(f"number of trainable parameters: {n_params}") # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") if args.blockwise_fused_optimizers: # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters. # This balances memory usage and management complexity. # split params into groups. currently different learning rates are not supported grouped_params = [] param_group = {} for group in params_to_optimize: named_parameters = list(flux.named_parameters()) assert len(named_parameters) == len(group["params"]), "number of parameters does not match" for p, np in zip(group["params"], named_parameters): # determine target layer and block index for each parameter block_type = "other" # double, single or other if np[0].startswith("double_blocks"): block_index = int(np[0].split(".")[1]) block_type = "double" elif np[0].startswith("single_blocks"): block_index = int(np[0].split(".")[1]) block_type = "single" else: block_index = -1 param_group_key = (block_type, block_index) if param_group_key not in param_group: param_group[param_group_key] = [] param_group[param_group_key].append(p) block_types_and_indices = [] for param_group_key, param_group in param_group.items(): block_types_and_indices.append(param_group_key) grouped_params.append({"params": param_group, "lr": args.learning_rate}) num_params = 0 for p in param_group: num_params += p.numel() accelerator.print(f"block {param_group_key}: {num_params} parameters") # prepare optimizers for each group optimizers = [] for group in grouped_params: _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group]) optimizers.append(optimizer) optimizer = optimizers[0] # avoid error in the following code logger.info(f"using {len(optimizers)} optimizers for blockwise fused optimizers") if train_util.is_schedulefree_optimizer(optimizers[0], args): raise ValueError("Schedule-free optimizer is not supported with blockwise fused optimizers") self.optimizer_train_fn = lambda: None # dummy function self.optimizer_eval_fn = lambda: None # dummy function else: _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) self.optimizer_train_fn, self.optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args) # prepare dataloader # strategies are set here because they cannot be referenced in another process. Copy them with the dataset # some strategies can be None train_dataset_group.set_current_strategies() # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意 n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers train_dataloader = torch.utils.data.DataLoader( train_dataset_group, batch_size=1, shuffle=True, collate_fn=collator, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers, ) # 学習ステップ数を計算する if args.max_train_epochs is not None: args.max_train_steps = args.max_train_epochs * math.ceil( len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps ) accelerator.print( f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}" ) # データセット側にも学習ステップを送信 train_dataset_group.set_max_train_steps(args.max_train_steps) # lr schedulerを用意する if args.blockwise_fused_optimizers: # prepare lr schedulers for each optimizer lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers] lr_scheduler = lr_schedulers[0] # avoid error in the following code else: lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする if args.full_fp16: assert ( args.mixed_precision == "fp16" ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" accelerator.print("enable full fp16 training.") flux.to(weight_dtype) if clip_l is not None: clip_l.to(weight_dtype) t5xxl.to(weight_dtype) # TODO check works with fp16 or not elif args.full_bf16: assert ( args.mixed_precision == "bf16" ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" accelerator.print("enable full bf16 training.") flux.to(weight_dtype) if clip_l is not None: clip_l.to(weight_dtype) t5xxl.to(weight_dtype) # if we don't cache text encoder outputs, move them to device if not args.cache_text_encoder_outputs: clip_l.to(accelerator.device) t5xxl.to(accelerator.device) clean_memory_on_device(accelerator.device) if args.deepspeed: ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=flux) # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( ds_model, optimizer, train_dataloader, lr_scheduler ) training_models = [ds_model] else: # accelerator does some magic # if we doesn't swap blocks, we can move the model to device flux = accelerator.prepare(flux, device_placement=[not self.is_swapping_blocks]) if self.is_swapping_blocks: accelerator.unwrap_model(flux).move_to_device_except_swap_blocks(accelerator.device) # reduce peak memory usage optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする if args.full_fp16: # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. # -> But we think it's ok to patch accelerator even if deepspeed is enabled. train_util.patch_accelerator_for_fp16_training(accelerator) # resumeする train_util.resume_from_local_or_hf_if_specified(accelerator, args) if args.fused_backward_pass: # use fused optimizer for backward pass: other optimizers will be supported in the future from .library import adafactor_fused adafactor_fused.patch_adafactor_fused(optimizer) for param_group, param_name_group in zip(optimizer.param_groups, param_names): for parameter, param_name in zip(param_group["params"], param_name_group): if parameter.requires_grad: def create_grad_hook(p_name, p_group): def grad_hook(tensor: torch.Tensor): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(tensor, args.max_grad_norm) optimizer.step_param(tensor, p_group) tensor.grad = None return grad_hook parameter.register_post_accumulate_grad_hook(create_grad_hook(param_name, param_group)) elif args.blockwise_fused_optimizers: # prepare for additional optimizers and lr schedulers for i in range(1, len(optimizers)): optimizers[i] = accelerator.prepare(optimizers[i]) lr_schedulers[i] = accelerator.prepare(lr_schedulers[i]) # counters are used to determine when to step the optimizer global optimizer_hooked_count global num_parameters_per_group global parameter_optimizer_map optimizer_hooked_count = {} num_parameters_per_group = [0] * len(optimizers) parameter_optimizer_map = {} for opt_idx, optimizer in enumerate(optimizers): for param_group in optimizer.param_groups: for parameter in param_group["params"]: if parameter.requires_grad: def grad_hook(parameter: torch.Tensor): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(parameter, args.max_grad_norm) i = parameter_optimizer_map[parameter] optimizer_hooked_count[i] += 1 if optimizer_hooked_count[i] == num_parameters_per_group[i]: optimizers[i].step() optimizers[i].zero_grad(set_to_none=True) parameter.register_post_accumulate_grad_hook(grad_hook) parameter_optimizer_map[parameter] = opt_idx num_parameters_per_group[opt_idx] += 1 # epoch数を計算する num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0): args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1 # 学習する # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps accelerator.print("running training") accelerator.print(f" num examples: {train_dataset_group.num_train_images}") accelerator.print(f" num batches per epoch: {len(train_dataloader)}") accelerator.print(f" num epochs: {num_train_epochs}") accelerator.print( f" batch size per device: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" ) # accelerator.print( # f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}" # ) accelerator.print(f" gradient accumulation steps = {args.gradient_accumulation_steps}") accelerator.print(f" total optimization steps: {args.max_train_steps}") progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps") self.global_step = 0 noise_scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift) noise_scheduler_copy = copy.deepcopy(noise_scheduler) if accelerator.is_main_process: init_kwargs = {} if args.wandb_run_name: init_kwargs["wandb"] = {"name": args.wandb_run_name} if args.log_tracker_config is not None: init_kwargs = toml.load(args.log_tracker_config) accelerator.init_trackers( "finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs, ) if self.is_swapping_blocks: accelerator.unwrap_model(flux).prepare_block_swap_before_forward() # For --sample_at_first #flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs) self.loss_recorder = train_util.LossRecorder() epoch = 0 # avoid error when max_train_steps is 0 self.tokens_and_masks = tokens_and_masks self.num_train_epochs = num_train_epochs self.current_epoch = current_epoch self.args = args self.accelerator = accelerator self.unet = flux self.vae = ae self.text_encoder = [clip_l, t5xxl] self.save_dtype = save_dtype def training_loop(break_at_steps, epoch): global optimizer_hooked_count steps_done = 0 #accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") progress_bar.set_description(f"Epoch {epoch + 1}/{num_train_epochs} - steps") current_epoch.value = epoch + 1 for m in training_models: m.train() for step, batch in enumerate(train_dataloader): current_step.value = self.global_step if args.blockwise_fused_optimizers: optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step with accelerator.accumulate(*training_models): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device, dtype=weight_dtype) else: with torch.no_grad(): # encode images to latents. images are [-1, 1] latents = ae.encode(batch["images"].to(ae.dtype)).to(accelerator.device, dtype=weight_dtype) # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): accelerator.print("NaN found in latents, replacing with zeros") latents = torch.nan_to_num(latents, 0, out=latents) text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None) if text_encoder_outputs_list is not None: text_encoder_conds = text_encoder_outputs_list else: # not cached or training, so get from text encoders tokens_and_masks = batch["input_ids_list"] with torch.no_grad(): input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]] text_encoder_conds = text_encoding_strategy.encode_tokens( flux_tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask ) if args.full_fp16: text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds] # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # get noisy model input and timesteps noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps( args, noise_scheduler_copy, latents, noise, accelerator.device, weight_dtype ) # pack latents and get img_ids packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input) # b, c, h*2, w*2 -> b, h*w, c*4 packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2 img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device) # get guidance: ensure args.guidance_scale is float guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device) # call model l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds if not args.apply_t5_attn_mask: t5_attn_mask = None if args.bypass_flux_guidance: flux_utils.bypass_flux_guidance(flux) with accelerator.autocast(): # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing) model_pred = flux( img=packed_noisy_model_input, img_ids=img_ids, txt=t5_out, txt_ids=txt_ids, y=l_pooled, timesteps=timesteps / 1000, guidance=guidance_vec, txt_attention_mask=t5_attn_mask, ) # unpack latents model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width) if args.bypass_flux_guidance: flux_utils.restore_flux_guidance(flux) # apply model prediction type model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas) # flow matching loss: this is different from SD3 target = noise - latents # calculate loss huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler) loss = train_util.conditional_loss(model_pred.float(), target.float(), args.loss_type, "none", huber_c) if weighting is not None: loss = loss * weighting if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight loss = loss * loss_weights loss = loss.mean() # backward accelerator.backward(loss) if not (args.fused_backward_pass or args.blockwise_fused_optimizers): if accelerator.sync_gradients and args.max_grad_norm != 0.0: params_to_clip = [] for m in training_models: params_to_clip.extend(m.parameters()) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) else: # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook lr_scheduler.step() if args.blockwise_fused_optimizers: for i in range(1, len(optimizers)): lr_schedulers[i].step() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) self.global_step += 1 current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず if len(accelerator.trackers) > 0: logs = {"loss": current_loss} train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True) accelerator.log(logs, step=self.global_step) self.loss_recorder.add(epoch=epoch, step=step, loss=current_loss, global_step=self.global_step) avr_loss: float = self.loss_recorder.moving_average logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if self.global_step >= break_at_steps: break steps_done += 1 if len(accelerator.trackers) > 0: logs = {"loss/epoch": self.loss_recorder.moving_average} accelerator.log(logs, step=epoch + 1) return steps_done return training_loop def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() add_logging_arguments(parser) train_util.add_sd_models_arguments(parser) # TODO split this train_util.add_dataset_arguments(parser, True, True, True) train_util.add_training_arguments(parser, False) train_util.add_masked_loss_arguments(parser) deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_sd_saving_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) add_custom_train_arguments(parser) # TODO remove this from here train_util.add_dit_training_arguments(parser) flux_train_utils.add_flux_train_arguments(parser) parser.add_argument( "--mem_eff_save", action="store_true", help="[EXPERIMENTAL] use memory efficient custom model saving method / メモリ効率の良い独自のモデル保存方法を使う", ) parser.add_argument( "--fused_optimizer_groups", type=int, default=None, help="**this option is not working** will be removed in the future / このオプションは動作しません。将来削除されます", ) parser.add_argument( "--blockwise_fused_optimizers", action="store_true", help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする", ) parser.add_argument( "--skip_latents_validity_check", action="store_true", help="skip latents validity check / latentsの正当性チェックをスキップする", ) parser.add_argument( "--cpu_offload_checkpointing", action="store_true", help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing / チェックポイント時にテンソルをCPUにオフロードする", ) return parser